Course Overview

Introduction to Structural Equation Modeling Using IBM SPSS Amos (V22) is a two day instructor-led classroom course that guides students through the fundamentals of using IBM SPSS Amos for the typical data analysis process. You will learn the basics of Structural Equation Modeling, drawing Diagrams in Amos Graphics, performing regression and confirmatory factor analysis in Amos, evaluating model fit, and ways to improve model fit.

Who Should Attend

This basic course is for:

Analysts with familiarity with Structural Equation Modeling

Anyone with little or no experience in using IBM SPSS Amos

Prerequisites

You should have:

Experience with Linear Regression and Factor Analysis

Experience using with IBM SPSS Amos is not necessary, though basic familiarity with Structural Equation Modeling would be helpful.

Course Content

Introduction to Structural Equation Modeling

Some Examples of SEM Models

Terminology in SEM

Drawing Diagrams in Amos Graphics

Launching Amos Graphics

Drawing the Diagram

Example – Sample Factor Analysis Path Diagram

Example – Multiple Regression Path Diagram

Regression Analysis in Amos

Setting up a Regression in Amos

Requesting a Linear Regression

Regression Output

Demonstration: Multiple Regression

Testing Model Adequacy

Implied versus Sample Moments

Requesting Implied and Sample Moments

Constraining the Regression Weight to Zero

Testing a Hypothesis with the Chi-Square Test

Displaying the Chi-Square Test in the Diagram

Degrees of Freedom

Verifying the Degrees of Freedom

Model Identification

Demonstration: Testing the Fit of a Path Analysis Model

Additional Fit Measures in Amos

Alternative FIT Measures

Demonstration: Fitting a Model with Multiple Regression

Confirmatory Factor Analysis in Amos

Latent vs. Observed Variables

Exploratory vs. Confirmatory Factor Analysis

Estimating and Identifying a Latent Model in CFA

Requesting a Confirmatory Factor Analysis

Demonstration of a Confirmatory Factor Analysis

The General Model

Requesting the General Model

Demonstration: General Model

Analyzing Data With Missing Values in Amos

Demonstration: How to Use the Full Information Maximum Likelihood Method to Handle Missing Values